Enhancing inertia and voltage regulation using converter-interfaced synchronous condensers at LCC-HVDC terminals
Bibliographic record
Abstract
Voltage and frequency regulation in weak ac systems connected to Line Commutated Converter (LCC)-based High Voltage Direct Current (HVdc) transmission systems presents significant challenges. Synchronous condensers (SCs) are commonly utilized to provide reactive power and inertia support. Although SCs effectively mitigate frequency deviations, their kinetic energy exchange is constrained to maintain synchronism, and excessive inertia may delay frequency restoration. Furthermore, SCs regulate voltage more slowly than power electronic devices such as STATCOMs. This paper investigates a proposed hybrid topology that combines a back-to-back (BtB) medium voltage DC (MVdc) system with an SC. By interfacing the SC to the ac network via a reconfigurable pair of voltage source converters (VSCs), the equivalent inertia and voltage regulation capabilities of the SC can be dynamically adjusted. This approach not only improves the short circuit current and allows fast voltage regulation through the STATCOM configuration, but also allows the SC frequency to deviate significantly from the frequency of the ac network. Consequently, the hybrid BtB-SC system emulates an SC with a substantially higher inertia rating than a directly connected SC. The performance of this hybrid system is evaluated and compared to a conventional SC system using Electromagnetic Transient (EMT) simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".